The package can download data for any of the 13 major climate models, but it also offers provides access to ensemble data derived from all models. We'll focus on this for our examples. Here we'll plot temperature and precipiation data for Great Britain for the past and two climate scenarios, A2 and B1.
Load the library
Grab temperature data
dat <- get_ensemble_temp("GBR", "annualavg", 1900, 2100)
Subset to the median percentile
dat <- subset(dat, dat$percentile == 50) dat$data <- do.call(c, dat$data)
Plot and note the past is the same for each scenario
ggplot(dat, aes(x=fromYear,y=data,group=scenario,colour=scenario)) + geom_point(size=4) + geom_path() + xlab("Year") + ylab("Annual Average Temperature in 20 year increments") + theme_grey(base_size = 16)
As you can see the A2 scenario of unchecked growth predicts a higher annual average temperature. We can look at the same kind of data except this time examining changes in precipitation.
dat2 <- get_ensemble_precip("GBR", "annualavg", 1900, 2100) dat2 <- subset(dat2, dat2$percentile == 50) dat2$data <- do.call(c, dat2$data) ggplot(dat2, aes(x = fromYear, y = data, group = scenario, colour = scenario)) + geom_point(size=4) + geom_path() + xlab("Year") + ylab("Annual Average precipitation in mm") + theme_grey(base_size = 16)
Here the difference between predicted increases in precipitation are less drastic when comparing the two different scenarios.